The field of medical imaging has seen significant advances since the time X-Rays were first used to determine anatomical abnormalities. Medical imaging hardware has progressed in the form of newer machines such as Medical Resonance Imaging (MRI) scanners, Computed Axial Tomography (CAT) scanners, etc. Because of large amount of image data generated by such modern medical scanners, there has been and remains a need for developing image processing techniques that can automate some or all of the processes to determine the presence of anatomical abnormalities in scanned medical images.
Digital medical images are constructed using raw image data obtained from a scanner, for example, a CAT scanner, an MRI, etc. Digital medical images are typically either a two-dimensional (“2-D”) image made of pixel elements or a three-dimensional (“3-D”) image made of volume elements (“voxels”). Such 2-D or 3-D images are processed using medical image recognition techniques to determine the presence of anatomical abnormalities such as cysts, tumors, polyps, etc. Given the amount of image data generated by any given image scan, it is preferable that an automatic technique should point out anatomical features in the selected regions of an image to a doctor for further diagnosis of any disease or condition.
Automatic image processing and recognition of structures within a medical image is generally referred to as Computer-Aided Detection (CAD). A CAD system can process medical images and identify anatomical structures or regions, including possible abnormalities (or candidates) for further review. Automatic detection of anatomical regions in medical images benefits a clinical workflow in various aspects. For example, high-resolution MR scanning range can be optimized based on the automatically-detected anatomical regions. Learning-based approaches can be used to detect such anatomical regions in a robust way. However, even for the same anatomy, the definition of a region-of-interest may be different across clinical sites (e.g., hospitals, imaging centers, etc.).
More particularly, each clinical site may implement an imaging or scanning protocol that standardizes the way in which images are acquired using the various modalities at that particular clinical site. The imaging protocol specifies, for example, coil selection, field-of-view (FOV), in-plane resolution, and other imaging parameters that are tailored to the specific clinical site's policies, settings and preferences. Different clinical sites may apply different imaging protocols that define the region-of-interest differently. In order to adapt to the different imaging protocols, learning-based approaches may need to be re-trained for each specific clinical site to ensure consistent scan quality. Such re-training at clinical sites is not always feasible, and can be a difficult or an impossible process due to various reasons. For example, there may be a lack of training data at the clinical site; computational resources may be limited; and so forth.
Therefore there is a need for improved systems and methods for automated or semi-automated anatomical region detection in medical images.